Which AI model is best for Hindi Transcriptions for Custom Dataset Hindi


We must follow a systematic approach to train a custom model using Hindi transcriptions. Here’s a comprehensive guide to creating a custom dataset, preprocessing the data, and training a model:

Step 1: Collecting and Preparing Data

  1. Data Collection:

    • Gather Hindi transcriptions from reliable sources such as Hindi books, subtitles, spoken corpora, etc.
    • Ensure a diverse dataset covering various topics, accents, and dialects.
  2. Data Annotation:

    • If the dataset includes audio, annotate the transcriptions accurately.
    • Use tools like ELAN, Praat, or custom annotation tools.
  3. Data Formatting:

    • Organize data into a structured format such as CSV, JSON, or plain text files.
    • For instance, create a CSV file with columns for audio_file_path and transcription.

Step 2: Preprocessing the Data

  1. Text Normalization:

    • Convert text to a consistent format: handle punctuation, case normalization, and remove special characters if needed.
    • Use libraries like indic-nlp-library for preprocessing Hindi text.
  2. Tokenization:

    • Tokenize the Hindi sentences into words or subwords.
    • Tools like SentencePiece or indic-nlp-library can be useful.
  3. Creating a Vocabulary:

    • Build a vocabulary of words or subwords from the transcriptions.
    • Limit the vocabulary size based on frequency to handle rare words effectively.

Step 3: Model Training

  1. Choose a Model:

    • Depending on your needs, select an appropriate model architecture:
      • For speech-to-text: Models like DeepSpeech, Wav2Vec 2.0.
      • For text-based tasks: Transformer models like BERT, GPT, or custom LSTM-based models.
  2. Environment Setup:

    • Set up a Python environment with necessary libraries like TensorFlow, PyTorch, HuggingFace Transformers, etc.
  3. Model Configuration:

    • Configure the model parameters, such as input size, hidden layers, learning rate, etc.
    • Split data into training, validation, and test sets.
  4. Training Loop:

    • Implement the training loop with batch processing, loss calculation, and optimization.
    • Regularly validate the model performance on the validation set to avoid overfitting.

Step 4: Evaluation and Fine-Tuning

  1. Model Evaluation:

    • Evaluate the model using appropriate metrics such as Word Error Rate (WER) for speech-to-text or accuracy/F1-score for text-based tasks.
    • Use the test set for final evaluation.
  2. Fine-Tuning:

    • Fine-tune the model on the specific domain data if required.
    • Experiment with hyperparameters and training strategies to improve performance.
  3. Error Analysis:

    • Analyze the errors to understand the model's weaknesses.
    • Focus on difficult examples and iteratively improve the model.

Step 5: Deployment

  1. Model Export:

    • Export the trained model to a suitable format (e.g., ONNX, TensorFlow SavedModel).
  2. Serving the Model:

    • Deploy the model using TensorFlow Serving, TorchServe, or custom Flask/Django applications.
  3. Monitoring:

    • Continuously monitor the model's performance in a real-world scenario and update the model as needed.

Example Code Snippet for Preprocessing and Training

Here’s an example code snippet demonstrating the preprocessing and training workflow using HuggingFace’s Transformers for a text-based model:

import pandas as pd from transformers import BertTokenizer, BertForSequenceClassification, Trainer, TrainingArguments from sklearn.model_selection import train_test_split import torch # Load and preprocess data df = pd.read_csv('hindi_transcriptions.csv') train_texts, val_texts, train_labels, val_labels = train_test_split(df['transcription'], df['label'], test_size=0.2) tokenizer = BertTokenizer.from_pretrained('bert-base-multilingual-cased') train_encodings = tokenizer(train_texts.tolist(), truncation=True, padding=True, max_length=128) val_encodings = tokenizer(val_texts.tolist(), truncation=True, padding=True, max_length=128) class HindiDataset(torch.utils.data.Dataset): def __init__(self, encodings, labels): self.encodings = encodings self.labels = labels def __getitem__(self, idx): item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()} item['labels'] = torch.tensor(self.labels[idx]) return item def __len__(self): return len(self.labels) train_dataset = HindiDataset(train_encodings, train_labels.tolist()) val_dataset = HindiDataset(val_encodings, val_labels.tolist()) # Model training model = BertForSequenceClassification.from_pretrained('bert-base-multilingual-cased') training_args = TrainingArguments( output_dir='./results', num_train_epochs=3, per_device_train_batch_size=8, per_device_eval_batch_size=16, warmup_steps=500, weight_decay=0.01, logging_dir='./logs', ) trainer = Trainer( model=model, args=training_args, train_dataset=train_dataset, eval_dataset=val_dataset ) trainer.train()


Training a custom model with Hindi transcriptions involves several steps from data collection and preprocessing to model training and deployment. Carefully handle each step to ensure a robust and accurate model. Adjust the process based on specific needs and available resources.

The choice of the best model for Hindi transcriptions largely depends on the specific task you are targeting. Here are some top models suitable for different tasks involving Hindi transcriptions:

1. Speech-to-Text (ASR - Automatic Speech Recognition)

For converting spoken Hindi into text, the following models are highly recommended:

Wav2Vec 2.0

  • Description: A state-of-the-art self-supervised learning model developed by Facebook AI.
  • Strengths: Excellent performance with limited labeled data due to its pretraining on large amounts of unlabeled data.
  • Implementation: Available through Hugging Face's Transformers library.
  • Example: facebook/wav2vec2-large-xlsr-53 is fine-tuned for multiple languages, including Hindi.

from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor import torch import librosa # Load pre-trained model and processor model = Wav2Vec2ForCTC.from_pretrained("facebook/wav2vec2-large-xlsr-53") processor = Wav2Vec2Processor.from_pretrained("facebook/wav2vec2-large-xlsr-53") # Load audio file audio_input, _ = librosa.load("path_to_hindi_audio.wav", sr=16000) # Preprocess audio input input_values = processor(audio_input, return_tensors="pt", padding="longest").input_values # Perform inference with torch.no_grad(): logits = model(input_values).logits # Decode predicted IDs to text predicted_ids = torch.argmax(logits, dim=-1) transcription = processor.batch_decode(predicted_ids) print(transcription)


  • Description: A sequence-to-sequence model optimized for ASR.
  • Strengths: Known for high accuracy and efficiency.
  • Implementation: Available via NVIDIA NeMo toolkit.

2. Text-Based Tasks (Translation, Text Generation, etc.)

For tasks like text generation, translation, or understanding Hindi text, transformer models are preferred:

mBERT (Multilingual BERT)

  • Description: A multilingual version of BERT supporting 104 languages, including Hindi.
  • Strengths: Versatile for various NLP tasks like text classification, translation, and more.
  • Implementation: Available through Hugging Face's Transformers library.

from transformers import BertTokenizer, BertForSequenceClassification, Trainer, TrainingArguments import torch # Load pre-trained model and tokenizer model = BertForSequenceClassification.from_pretrained('bert-base-multilingual-cased') tokenizer = BertTokenizer.from_pretrained('bert-base-multilingual-cased') # Example Hindi text text = "यह एक उदाहरण वाक्य है।" # Preprocess text inputs = tokenizer(text, return_tensors='pt', padding=True, truncation=True) # Perform inference with torch.no_grad(): outputs = model(**inputs) # Get predictions logits = outputs.logits predictions = torch.argmax(logits, dim=-1) print(predictions)

mT5 (Multilingual T5)

  • Description: A multilingual version of T5 (Text-to-Text Transfer Transformer) supporting many languages.
  • Strengths: Suitable for various tasks like translation, summarization, and more.
  • Implementation: Available through Hugging Face's Transformers library.
from transformers import T5Tokenizer, T5ForConditionalGeneration # Load pre-trained model and tokenizer model = T5ForConditionalGeneration.from_pretrained('google/mt5-small') tokenizer = T5Tokenizer.from_pretrained('google/mt5-small') # Example Hindi text text = "translate English to Hindi: This is a sample sentence." # Preprocess text inputs = tokenizer.encode(text, return_tensors='pt') # Perform inference with torch.no_grad(): outputs = model.generate(inputs) # Decode the generated tokens decoded_output = tokenizer.decode(outputs[0], skip_special_tokens=True) print(decoded_output)


  • Description: A BERT-based model specifically trained on Indian languages, including Hindi.
  • Strengths: Tailored for Indian languages, providing improved performance over general multilingual models.
  • Implementation: Available through AI4Bharat and Hugging Face.


  • Wav2Vec 2.0 is highly recommended for ASR tasks involving Hindi.
  • mBERT, mT5, and IndicBERT are excellent choices for text-based tasks like translation, classification, and generation.

The choice of model ultimately depends on the specific requirements of your task and the availability of pre-trained models and datasets. Experimenting with a few models and evaluating their performance on your dataset is the best approach to determine the most suitable one.

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